Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A Low-Scan Incremental Association Rule Maintenance Method Based on the Apriori Property
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Mining Sequential Alarm Patterns in a Telecommunication Database
DBTel '01 Proceedings of the VLDB 2001 International Workshop on Databases in Telecommunications II
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Building intrusion pattern miner for Snort network intrusion detection system
Journal of Systems and Software
Privacy preserving data mining of sequential patterns for network traffic data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Sequence Data Mining
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Event prediction is one of the most challenging problems in network monitoring systems. This type of inductive knowledge provides monitoring systems with valuable real time predictive capabilities. By obtaining this knowledge, system and network administrators can anticipate and prevent failures. In this paper we present a prediction module for the monitoring software Osmius (www.osmius.net). Osmius has been developed by Peopleware (peopleware.es) under GPL licence. We have extended the Osmius database to store the knowledge we obtain from the algorithms in a highly parametrized way. Thus system administrators can apply the most appropriate settings for each system. Results are presented in terms of positive predictive values and false discovery rates over a huge event database. They confirm that these pattern mining processes will provide network monitoring systems with accurate real time predictive capabilities.